Kernel projection algorithm for large-scale SVM problems

Journal of Computer Science and Technology

Volumn 17, Issue 5, 2002, Pages 556-564

  Wang, Jiaqi ^a   Tao, Qing ^a   Wang, Jue ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Feature mapping; Fixed point; MNP; NPP; Projection; SVM; Universal kernel

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; CONFORMAL MAPPING; CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING; SET THEORY; STATISTICAL METHODS;

NEAREST POINT PAIR PROBLEMS (NPP); SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

EID: 0036739354     PISSN: 10009000     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02948824     Document Type: Article

References (13)

1. Vapnik V. Statistical Learning Theory. Addison-Wiley, 1998.
2. Bennett R, Bredensteiner E J. Geometry in Learning. Tech. Report, Department of Mathematical Sciences, Rennselaer Polytechnic Institute, New York, 1996.
3. Keerthi S S, Shevade S K, Bhattacharyya C et al. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Transactions on Neural Networks, 2000, 11(1): 124-136.
4. Crisp D J, Burges C J C. A geometric interpretation of μ-SVM classifiers. NIPS 12, 2000, pp.244-250.
5. Tao Qing, Sun Demin, Fan Jinsong. The maximal margin linear classifier based on the contraction of the closed convex hull. To appear in Journal of Software.
6. n. Computers and Mathematics with Applications, 2000, 40: 971-983.
7. Platt J. Fast training of support vector machines using sequential minimal optimization in Advances in Kernel Methods - Support Vector Learning. Scholkopf B, Burges C J C, Smola A J (eds.), Cambridge, MA: MIT Press, 1999, pp.185-208.
8. Gilbert E G. Minimizing the quadratic form on a convex set. SIAM J. Contr., 1966, 4: 61-79.
9. Mitchell B F, Dem'yanov V F, Malozemov V N. Finding the point of a polyhedron closest to the origin. SIAM J. Contr., 1974, 12: 19-26.
10. Gilbert E G, Johnson D W, Keerthi S S. A fast procedure computing the distance between complex objects in three dimension space. IEEE J. Robot. Automat., 1988, 4: 193-203.
11. Muller K R et al. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks, 2001, 12(2): 181-201.
12. Tao Qing, Wu Gaowei, Wang Jue. The theoretical analysis of kernel technique and kernel covering approach. Technique Report, Institute of Automation, Chinese Academy of Sciences, 2001.
13. Tao Qing, Wang Jiaqi, Wang Jue. Soft kernel projection algorithm for support vector machines. Technique Report, Institute of Automation, Chinese Academy of Sciences, 2001.